Fuzzy Clustering Segmentation Algorithm Research for Biomedical Image Based on Artificial Life

Long Wang; Bin Liu; Mo Yuan Yang

doi:10.4028/www.scientific.net/AMM.571-572.213

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 571-572Fuzzy Clustering Segmentation Algorithm Research...

Fuzzy Clustering Segmentation Algorithm Research for Biomedical Image Based on Artificial Life

Abstract:

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the biomedical image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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Periodical:

Applied Mechanics and Materials (Volumes 571-572)

Pages:

213-216

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.571-572.213

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Online since:

June 2014

Authors:

Long Wang, Bin Liu*, Mo Yuan Yang

Keywords:

Artificial Life, Biomedical Image, FCM, Image Segmentation

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* - Corresponding Author

References

[1] Yujin ZHANG. Image Processing and Analysis [M], Beijing: Tsinghua University Press, (1999).

Google Scholar

[2] Z. Ameur, Image coding in view of high level segmentation : Application to satellite images [Codage des images en vue d'une segmentation de haut niveau: Application aux images satellitaires], Ph. D, Algeria, Tizi Ouzou : Mouloud Mammeri University, September (2009).

DOI: 10.15676/ijeei.2014.6.1.7

Google Scholar

[3] Bezdek J C, Hathaway R J. Progressive sampling schemes for approximate clustering in very large data sets, [C]. Proceedings of 2008 IEEE International Conference on Fuzzy Systems, 2008, 1: 15-21.

DOI: 10.1109/fuzzy.2004.1375677

Google Scholar

[4] Chuang K S, Tzeng H L , Chen S, etal. Fuzzy c-means clustering with spatial information for image segmentation [J]. Computerized Medical Imaging and Graphics, 2006, 30: 9216.

DOI: 10.1016/j.compmedimag.2005.10.001

Google Scholar

[5] A. Rybak, V. I. Gusakova, A. Golovan, L. N. Podladchikova and N. A. Shevtsova, A model of attention-guided visual perception and recognition, Vis. Res. 38. 2009, 2387-2400.

DOI: 10.1016/s0042-6989(98)00020-0

Google Scholar